Deepseek-R1: DESTROYS O1 & Sonnet 3.5 – The True Open-Source Coding King Is Here!

The video script discusses the Deepseek-R1, an open-source AI model that claims to rival the capabilities of O1 and Sonnet 3.5. The presenter tests the R1 as a coding assistant, comparing its performance in tasks like creating a terms and conditions page and improving a contact us page. The R1 shows impressive logic and analysis capabilities, though it is slower than Sonnet 3.5. The presenter highlights the R1's cost-effectiveness, with significantly lower pricing than O1. Despite its speed, the R1 is praised for its potential as a cheaper alternative for coding tasks.

Takeaways

• 😀 DeepSeek R1, an open-source AI model, claims to rival the capabilities of O1 and compete with Sonnet 3.5 in coding.
• 😀 The R1 model can be accessed for free through Chad Deep or Open Router, with more options expected soon.
• 😀 In benchmark tests, DeepSeek R1 shows impressive results, closely following O1 and surpassing other models like Gemini.
• 😀 When tested as a coding assistant, R1 demonstrated strong capabilities in creating responsive UIs and handling translations.
• 😀 Pricing for DeepSeek R1 is significantly cheaper than Sonnet 3.5, making it a cost-effective alternative.
• 😀 The model can be run locally on various hardware setups, with options ranging from 4GB to 21 billion parameters.
• 😀 Despite its capabilities, R1's response speed is slower compared to Sonnet 3.5, which may be a drawback for some users.
• 😀 DeepSeek R1 provides useful feedback on code structure and suggests improvements, such as adding Swagger documentation.
• 😀 The model's open-source nature allows for commercial use and customization, offering flexibility to developers.
• 😀 Overall, DeepSeek R1 is a promising open-source alternative to proprietary models, though improvements in speed are needed for wider adoption.

Q & A

What is the Deepseek-R1, and how does it compare to the O1 and Sonnet 3.5 models?

The Deepseek-R1 is a fully open-source AI model that claims to rival the power of the O1 model and compete with the Sonnet 3.5 in terms of encoding capability. It has shown impressive performance in benchmarks, placing just behind the O1 model and above other models like the Gemini experiment and the Gemini 2 flash thinking experiment model.

How can one access and use the Deepseek-R1 model for free?

There are multiple ways to access and use the Deepseek-R1 model for free. One can go to chad.deep.com, create an account, and use the Deepseek service. Another option is to use Open Router, which provides access to the Deepseek-R1 model. In the future, more providers like Hyperbolic Deep Inference may also offer the model.

What are the pricing differences between the Deepseek-R1 and the Sonnet 3.5 models?

The Deepseek-R1 model is significantly cheaper than the Sonnet 3.5 model. For 1 million input tokens, the Deepseek-R1 costs 55 cents, while the Sonnet 3.5 costs $3. For 1 million output tokens, the Deepseek-R1 costs $2, and the Sonnet 3.5 costs $15. Additionally, the Deepseek-R1 has lower caching costs.

How does the Deepseek-R1 perform in coding tasks compared to the Sonnet 3.5?

The Deepseek-R1 performs very well in coding tasks, showing a good understanding of logic and the ability to generate functional code. It can handle tasks like creating a terms and conditions page and improving the UI of a contact us page. However, it can be slower in terms of response time compared to the Sonnet 3.5.

What are some of the challenges faced when using the Deepseek-R1 model?

One of the main challenges is the slower response time, which can be a significant drawback for developers who need quick feedback. Additionally, the model may sometimes get stuck on errors when given slightly larger tasks, requiring the task to be broken down into smaller parts.

Can the Deepseek-R1 model be run locally, and what are the requirements?

Yes, the Deepseek-R1 model can be run locally. There are different versions available, such as the 7B, 8B, 32B, and 70B models, with varying sizes and requirements. For example, the 32B model is similar to the O1 mini, and the 70B model uses the Lama model. Running these models locally requires significant computational resources, such as an RTX 3080 for the 32B model.

How does the Deepseek-R1 model handle UI design tasks?

The Deepseek-R1 model can handle UI design tasks reasonably well, although it may not always produce the most creative or modern designs. It can add elements like headers, footers, and icons, and ensure that the design supports different themes and translations.

What kind of feedback does the Deepseek-R1 model provide for code analysis?

The Deepseek-R1 model provides decent feedback for code analysis. It can review the structure of a project, suggest improvements like adopting Zod for validation, centralizing error handling, and adding Swagger documentation. It also provides feedback on security hardening and testing structure.

Is the Deepseek-R1 model suitable for backend development?

The Deepseek-R1 model shows promise for backend development, although its speed may be a limiting factor. It can provide useful feedback and suggestions for improving backend code and structure. However, further testing and improvements in response time would be beneficial before fully switching to the Deepseek-R1 for backend development.

What are the future prospects for the Deepseek-R1 model?

The Deepseek-R1 model has shown significant potential in a short amount of time, catching up to closed-source models like the O1. Its open-source nature and low cost make it an attractive alternative for developers. With further improvements in speed and functionality, the Deepseek-R1 could become a leading choice for AI-assisted coding.

Outlines

00:00

🚀 Deeps R1 vs. Sonet 3.5: A New AI Benchmark

This paragraph introduces the groundbreaking Deeps R1 model, which has entered the AI scene claiming to rival the power of Sonet 3.5, especially for coding assistance. The narrator tests the R1 against Sonet 3.5 by using it for coding tasks in Rook line, discussing its ease of use and multiple free access methods (such as via Deep's API or OpenRouter). The initial benchmarks show R1 as highly competitive, coming close to Sonet 3.5 in coding performance, and slightly outperforming the older O Braeview model. However, the narrator decides to focus on the live benchmarks, which suggest that Sonet 3.5 still holds the top spot for coding tasks. The narrator then prepares their environment for testing R1's capabilities.

05:01

💡 Troubleshooting and R1's Translation Features

In this paragraph, the narrator shares their initial experience using the Deeps R1 model for a basic task—creating a responsive 'Terms and Conditions' page. Although the model made a few errors (such as importing an unnecessary header or misunderstanding language switching), it handled the task quickly after being guided. The model was able to add translation support for both Arabic and English, showing rapid adaptation. The narrator highlights this as an improvement compared to other models like Sonet 3.5, which sometimes struggled with language-related tasks.

10:03

💸 Cost Analysis and Comparison with Sonet 3.5

This paragraph dives into the pricing models for Deeps R1 and Sonet 3.5, comparing the costs for 1 million input and output tokens. Deeps R1 proves to be significantly cheaper, with costs as low as 55 cents for 1 million input tokens and $2 for 1 million output tokens, compared to Sonet 3.5’s much higher prices. The narrator also discusses the possibility of running R1 locally using different model versions available on Hugging Face, offering a cheaper alternative to Sonet 3.5 for developers. The narrator emphasizes the open-source nature of Deeps R1, which is seen as a breakthrough for the AI community.

15:04

🎨 UI Design Test: R1 vs. Sonet 3.5

Here, the narrator tests R1's ability to design a 'Contact Us' page, asking for improvements in design using the header, footer, and theme-switching logic. Initially, R1 runs into errors but is able to handle them after breaking the task into smaller pieces. While the final result is functional, the design isn’t as creative or modern as Sonet 3.5’s output, but the page still supports the dark and light modes and doesn’t break. The narrator expresses satisfaction with the result but plans to request more creative design elements.

⚡ Speed and Latency Issues with Deeps R1

The narrator discusses the main limitation they’ve encountered while working with Deeps R1: the latency. Despite receiving functional results, there are occasional delays of up to 30 seconds for responses, which the narrator finds too slow for their development needs. They also notice an issue with Deeps R1's interface, where a small icon indicates that the prompts and code are being used to train future models. The narrator finds this somewhat annoying but understands it. Despite the latency, they acknowledge the impressive capabilities of R1.

🔧 Feedback and Suggestions from R1

In this paragraph, the narrator seeks feedback from R1 on improving a server folder structure and receives a thorough analysis of their project. R1 identifies the use of the MVC pattern with Express.js and provides suggestions for improvement, such as adding Swagger documentation and adopting Zod for validation. The feedback includes suggestions for security hardening, testing, and other improvements, which the narrator finds valuable but not overly critical. The feedback also suggests a few minor fixes, but the project’s architecture is deemed sound overall.

📝 Deeps R1’s Impressive Growth and Open-Source Future

The narrator concludes by reflecting on the rapid progress of open-source AI models, particularly Deeps R1, which has developed into a highly capable tool in just a few months. They praise R1 for being an affordable alternative to the expensive Sonet 3.5 model, offering significant cost savings. The narrator also notes that while Deeps R1 is not yet as fast as Sonet 3.5, it shows great promise, especially for coding tasks. The narrator intends to continue using Sonet 3.5 for now but sees the future of R1 as bright, particularly for backend applications.

Keywords

Deepseek-R1

Deepseek-R1 is a fully open-source AI model that claims to rival the power of the O1 model and compete with the Sonnet 3.5 in terms of encoding capability. In the video, the presenter tests Deepseek-R1 as a coding assistant and compares its performance to the Sonnet 3.5. For example, the presenter mentions that Deepseek-R1 is number two on the leaderboard for coding models, just behind the Sonnet 3.5.

Open-source

Open-source refers to software or models whose source code is made available to the public, allowing anyone to use, modify, and distribute it. The video highlights that Deepseek-R1 is an open-source model, which means developers can access and potentially improve it. The presenter mentions multiple ways to use Deepseek-R1 for free, emphasizing its accessibility.

Coding assistant

A coding assistant is an AI tool that helps developers write code by suggesting code snippets, fixing errors, and providing explanations. In the video, the presenter uses Deepseek-R1 as a coding assistant in Rookline to create a terms and conditions page and improve a contact us page. The presenter evaluates how well Deepseek-R1 performs these tasks compared to the Sonnet 3.5.

Benchmark

A benchmark is a standard or reference point used to measure the performance of something. In the video, the presenter refers to the live benchmark to compare the performance of Deepseek-R1 with other models like the O1 and Sonnet 3.5. The presenter notes that Deepseek-R1 is just behind the O1 model in the global average but ahead of other models.

Responsive design

Responsive design refers to a web design approach that ensures a website or application looks good and functions well on various devices and screen sizes. In the video, the presenter asks Deepseek-R1 to create a terms and conditions page that is responsive and can switch between dark and light modes. This demonstrates Deepseek-R1's ability to handle responsive design tasks.

Translation

Translation is the process of converting text from one language to another. The video mentions that Deepseek-R1 was able to add translations to a page quickly, which is an improvement over the Sonnet 3.5. The presenter gives an example of how Deepseek-R1 added translations to the Arabic and English files without issues.

Pricing

Pricing refers to the cost associated with using a service or product. The video compares the pricing of Deepseek-R1 with the Sonnet 3.5, highlighting that Deepseek-R1 is significantly cheaper. For example, the presenter mentions that Deepseek-R1 costs 55 cents for 1 million input tokens and $2 for 1 million output tokens, while the Sonnet 3.5 costs $15 for 1 million input tokens and $60 for 1 million output tokens.

Local deployment

Local deployment refers to running a model or application on a local machine rather than relying on a cloud service. The video mentions that Deepseek-R1 can be run locally, which is an advantage for developers who prefer not to use cloud services. The presenter lists different sizes of the Deepseek-R1 model available for local deployment, such as the 7B, 8B, and 32B models.

UI design

UI design, or user interface design, involves creating the visual elements and layout of a user interface. In the video, the presenter tests Deepseek-R1's UI design capabilities by asking it to improve a contact us page. The presenter notes that while the initial design was functional, it was not as creative or modern as desired.

Latency

Latency refers to the delay between a request being sent and a response being received. The video mentions that Deepseek-R1 has a latency of about 5 to 6 seconds, which the presenter finds to be relatively slow compared to the Sonnet 3.5. The presenter notes that this latency can be a drawback when using Deepseek-R1 for coding tasks.

Highlights

DeepSeek R1 claims to rival the O1 model and compete with Sonnet 3.5 in coding capabilities.

DeepSeek R1 is a fully open-source model, available for free through DeepSeek's website or OpenRouter.

Benchmark results show DeepSeek R1 is just behind the O1 model and above the O1 Braev model in global average.

DeepSeek R1 is the new state-of-the-art model in coding, surpassing Sonnet 3.5 in the Adar later board.

DeepSeek R1 can be run locally on various hardware configurations, with models ranging from 7B to 67B parameters.

DeepSeek R1 is significantly cheaper than the O1 model, with pricing at 55 cents for 1 million input tokens and $2 for 1 million output tokens.

DeepSeek R1 demonstrates impressive logic and analysis capabilities, providing good feedback on code structure and improvements.

Despite its capabilities, DeepSeek R1 is slower in response time compared to Sonnet 3.5, with latency averaging around 5-6 seconds.

DeepSeek R1 is capable of creating responsive UIs and handling theme switching between dark and light modes.

DeepSeek R1 can improve UI design by adding icons and hover effects, though it may require breaking down tasks into smaller steps.

DeepSeek R1 suggests adopting Zod for validation, centralizing error handling, and adding Swagger documentation for better project structure.

DeepSeek R1 is a viable alternative to Sonnet 3.5 for coding tasks, offering a cheaper and almost equally capable open-source model.

The speed of response is the main factor preventing a complete switch to DeepSeek R1, but it is still a valuable tool for backend development.

DeepSeek R1's open-source nature allows for commercial use and customization, making it a flexible choice for developers.

DeepSeek R1's performance in coding tasks is on par with or better than existing models, despite its slower response times.

DeepSeek R1's ability to analyze and provide feedback on code structure and improvements is a significant advantage for developers.